Date of Award
8-2013
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Civil Engineering
Committee Chair/Advisor
Juang, C. Hsein
Committee Member
Pang , Weichiang
Committee Member
Huang , Yongxi
Abstract
This thesis is aimed at applying the probabilistic approaches for back analysis of geotechnical systems. First, a probabilistic back-analysis of a recent slope failure at a site on Freeway No. 3 in northern Taiwan is presented. The Markov Chain Monte Carlo (MCMC) simulation is used to back-calculate the geotechnical strength parameters and the anchor force. These inverse analysis results, which agree closely with the findings of the post-event investigations, are then used to validate the maximum likelihood method, a computationally more efficient back-analysis approach. The improved knowledge of the geotechnical strength parameters and the anchor force gained through the probabilistic inverse analysis better elucidate the slope failure mechanism, which provides a basis for a more rational selection of remedial measures.
Then the maximum likelihood principle is adapted to formulate an efficient framework for probabilistic back analysis of soil parameters in a braced excavation using multi-stage observations. The soil parameters are updated using the observations of the maximum ground settlement and/or wall deflection measured in a staged excavation. The updated soil parameters are then used to refine the predicted wall and ground responses in the subsequent excavation stages, as well as to assess the building damage potential at the final excavation stage. Case study shows that the proposed approach is effective in improving the predictions of the excavation-induced wall and ground responses. More-accurate predictions of the wall and ground responses, in turn, lead to a more accurate assessment of the damage potential of buildings adjacent to the excavation.
Recommended Citation
Wang, Lei, "PROBABILISTIC BACK ANALYSIS OF GEOTECHNICAL SYSTEMS" (2013). All Theses. 1727.
https://open.clemson.edu/all_theses/1727